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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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Optimized Deep Learning Approach for image Compression Using Compressed Sensing Technique with Convolutional Autoencoders 2

Optimized Deep Learning Approach for Image Compression Using...

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2nd IEEE International conference on data Science and Network Security, ICDSNS 2024

作者： Puttegowda, Kiran veeraprathap, v. Sequeira, Sharmila Shanthi Aruna, B. Kirankumarhumse Sunil Kumar, D.S. Sudheesh, K.v. Vidyavardhaka College of Engineering Department of Ece Karnataka Mysuru India Atme College of Engineering Department of Ece Karnataka Mysuru India Nitte Meenakshi Institute of Technology Department of Cse Karnataka Bengaluru India Bgs Institute of Technology Adichunchanagiri University Department of Ece Karnataka Mandya India Anatta Innovations Bgs B-School Bengaluru India

ISBN: (纸本)9798350373110

image compression is crucial for efficient storage and transmission of visual data. Traditional methods like JPEG use transform coding, which may result in loss of fine details. Compressed sensing (CS) offers an alternative by exploiting signal sparsity, allowing for significant data reduction without substantial loss of information. Compressed sensing (CS) has proven to be an important technique in signal acquisition, especially in contexts in which sensor quality or the maximum possible duration of the measurement is limited. In this paper, deep learning techniques are used to improve compressive sensing in the context of image acquisition. In a existing approaches deployed stacked denoising autoencoders capable of reconstructing images considerably faster than conventional iterative methods. Apart from reviewing this approach, a possible extension using convolutional autoencoders inspired by the popular vGGnet architecture is discussed. Instead of learning models from scratch, a simple yet effective way for adapting available filters used in imageNet classification is presented. By reformulation of the autoencoder structure in terms of a fully convolutional network can be adapted to arbitrarily large images for efficient learning of the measurement matrix and sparsity basis. Finally, experimental results indicate that the suggested approach outperforms current state-of-the-art methods across several metrics: reconstruction accuracy, time efficiency, and noise reduction capability. Particularly noteworthy is its exceptional reconstruction accuracy even with a limited number of measurements. © 2024 IEEE.

关键词： Noise abatement

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9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th International Skin Imaging Collaboration Workshop, ISIC 2024, 7th International Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for HealTHcare Workshop, EARTH 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

The proceedings contain 23 papers. The special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical image Computing, Embodied AI and Robotics for HealTHcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. The topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen data Properties with Diffusion Autoencoder Embeddings Using Spine MRI data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAvs: Debugging vision Models Using Text;evaluating visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues reconstruction with Gaussian Splatting;vISAGE: video Synthesis Using Action Graphs for Surgery;a Review of 3D reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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COvID-19 Pneumonia Classification with Transformer from incomplete Modalities 26th

COVID-19 Pneumonia Classification with Transformer from Inco...

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26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Carbonell, Eduard Lloret Shen, Yiqing Yang, Xin Ke, Jing Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9783031439032;9783031439049

COvID-19 is a viral disease that causes severe acute respiratory inflammation. Although with less death rate, its increasing infectivity rate, together with its acute symptoms and high number of infections, is still attracting growing interests in the image analysis of COvID-19 pneumonia. Current accurate diagnosis by radiologists requires two modalities of X-Ray and Computed Tomography (CT) images from one patient. However, one modality might miss in clinical practice. In this study, we propose a novel multi-modality model to integrate X-Ray and CT data to further increase the versatility and robustness of the AI-assisted COvID-19 pneumonia diagnosis that can tackle incomplete modalities. We develop a Convolutional Neural Networks (CNN) and Transformers hybrid architecture, which extracts extensive features from the distinct data modalities. This classifier is designed to be able to predict COvID-19 images with X-Ray image, or CT image, or both, while at the same time preserving the robustness when missing modalities are found. Conjointly, a new method is proposed to fuse three-dimensional and two-dimensional images, which further increase the feature extraction and feature correlation of the input data. Thus, verified with a real-world public dataset of BIMCv-COvID19, the model outperform state-of-the-arts with the AUC score of 79.93%. Clinically, the model has important medical significance for COvID-19 examination when some image modalities are missing, offering relevant flexibility to medical teams. Besides, the structure may be extended to other chest abnormalities to be detected by X-ray or CT examinations. Code is available at https://***/edurbi/MICCAI2023.

关键词： COvID-19 Pneumonia Classification Hybrid CNN-Transformer Multi-modality

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JR2Net: Joint Monocular 3D Face reconstruction and Reenactment 37

JR2Net: Joint Monocular 3D Face Reconstruction and Reenactme...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Shang, Jiaxiang Zeng, Yu Qiao, Xin Wang, Xin Zhang, Runze Sun, Guangyuan Patel, vishal Fu, Hongbo Hong Kong Univ Sci & Technol Hong Kong Peoples R China Johns Hopkins Univ Baltimore MD USA Tencent Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781577358800

Face reenactment and reconstruction benefit various applications in self-media, vR, etc. Recent face reenactment methods use 2D facial landmarks to implicitly retarget facial expressions and poses from driving videos to source images, while they suffer from pose and expression preservation issues for cross-identity scenarios, i.e., when the source and the driving subjects are different. Current self-supervised face reconstruction methods also demonstrate impressive results. However, these methods do not handle large expressions well, since their training data lacks samples of large expressions, and 2D facial attributes are inaccurate on such samples. To mitigate the above problems, we propose to explore the inner connection between the two tasks, i.e., using face reconstruction to provide sufficient 3D information for reenactment, and synthesizing videos paired with captured face model parameters through face reenactment to enhance the expression module of face reconstruction. In particular, we propose a novel cascade framework named JR2Net for Joint Face reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. In the end, we train an expression tracking network based on our synthesized videos composed by image-face model parameter pairs. Such an expression tracking network can further enhance the coarse face reconstruction. Extensive experiments show that our JR2Net outperforms the state-of-the-art methods on several face reconstruction and reenactment benchmarks.

关键词： image reconstruction

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PPIDSG: A Privacy-Preserving image Distribution Sharing Scheme with GAN in Federated Learning 38

PPIDSG: A Privacy-Preserving Image Distribution Sharing Sche...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Ma, Yuting Yao, Yuanzhi Xu, Xiaohua Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China

ISBN: (纸本)1577358872

Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In this paper, we conduct reconstruction attacks and enhance inference attacks on various datasets to better understand that sharing trained classification model parameters to a central server is the main problem of privacy leakage in FL. To tackle this problem, a privacy-preserving image distribution sharing scheme with GAN (PPIDSG) is proposed, which consists of a block scrambling-based encryption algorithm, an image distribution sharing method, and local classification training. Specifically, our method can capture the distribution of a target image domain which is transformed by the block encryption algorithm, and upload generator parameters to avoid classifier sharing with negligible influence on model performance. Furthermore, we apply a feature extractor to motivate model utility and train it separately from the classifier. The extensive experimental results and security analyses demonstrate the superiority of our proposed scheme compared to other state-of-the-art defense methods. The code is available at https://***/ytingma/PPIDSG.

关键词： Classification (of information)

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Autonomous vehicle Lane Following Network based on Multimodal Sensor Fusion and LiDAR reconstruction in Extreme Lighting and Snowing Environments 4

Autonomous Vehicle Lane Following Network based on Multimoda...

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4th IEEE International conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Padmapriya, J. Meera, v. Murugavalli, S. Karthikeyan, L. Panimalar Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Mechanical Engineering Chennai India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387063

The self-driving car industry is gaining attention for its role in motion planning technology. Deep learning approaches have been implemented to plan autonomous vehicles' motion, but their effectiveness depends on the data used in training. Despite advancements, fully autonomous vehicles still face limitations, such as lighting-related issues affecting camera image accuracy, and LiDARs are negatively impacted by challenging weather circumstances like snow in varied snowfall conditions, thus becoming imperative to precisely control the motion of the Autonomous vehicle. To address this, an architecture based on deep-learning networks is proposed for lane-following on roads, consisting of LiDAR De-snowing, Multi-modal Sensor Fusion, vGG16, and gated recurrent units (GRUs). The Point reconstruction Network (PR-Net) rebuilds points from neighbors, while the reconstruction Difficulty Network forecasts reconstruction difficulty. Radar measurements and de-snowed LiDAR point clouds are projected onto the front-view image plane, fed into vGG16 and GRU output layers, and processed into two fully connected layers for steering and speed parameters estimation. © 2024 IEEE.

关键词： Snow

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Self-Supervised 3D Human Mesh Recovery from a Single image with Uncertainty-Aware Learning 38

Self-Supervised 3D Human Mesh Recovery from a Single Image w...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Yan, Guoli Zhong, Zichun Hua, Jing Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)1577358872

Despite achieving impressive improvement in accuracy, most existing monocular 3D human mesh reconstruction methods require large-scale 2D/3D ground-truths for supervision, which limits their applications on unlabeled in-the-wild data that is ubiquitous. To alleviate the reliance on 2D/3D ground-truths, we present a self-supervised 3D human pose and shape reconstruction framework that relies only on self-consistency between intermediate representations of images and projected 2D predictions. Specifically, we extract 2D joints and depth maps from monocular images as proxy inputs, which provides complementary clues to infer accurate 3D human meshes. Furthermore, to reduce the impacts from noisy and ambiguous inputs while better concentrate on the high-quality information, we design an uncertainty-aware module to automatically learn the reliability of the inputs at body-joint level based on the consistency between 2D joints and depth map. Experiments on benchmark datasets show that our approach outperforms other state-of-the-art methods at similar supervision levels.

关键词： Mesh generation

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DEEP LEARNING BASED WORKFLOW FOR ACCELERATED INDUSTRIAL X-RAY COMPUTED TOMOGRAPHY 30

DEEP LEARNING BASED WORKFLOW FOR ACCELERATED INDUSTRIAL X-RA...

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30th IEEE International conference on image Processing (ICIP)

作者： Rahman, Obaidullah venkatakrishnan, Singanallur v. Scime, Luke Brackman, Paul Frederick, Curtis Dehoff, Ryan Paquit, vincent Ziabari, Amirkoushyar Oak Ridge Natl Lab ORNL Oak Ridge TN 37830 USA Carl Zeiss Ind Metrol LLC Maple Grove MN 55369 USA

ISBN: (纸本)9781728198354

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

关键词： Computerized tomography

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Unsupervised Learning from incomplete Measurements for Inverse Problems 36

Unsupervised Learning From Incomplete Measurements for Inver...

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36th conference on Neural Information Processing Systems (NeurIPS)

作者： Tachella, Julian Chen, Dongdong Davies, Mike CNRS Lab Phys Lyon France ENSL Lyon France Univ Edinburgh Sch Engn Edinburgh Midlothian Scotland

ISBN: (纸本)9781713871088

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be overcome by using measurements from multiple operators. While this idea has been successfully applied in various applications, a precise characterization of the conditions for learning is still lacking. In this paper, we fill this gap by presenting necessary and sufficient conditions for learning the underlying signal model needed for reconstruction which indicate the interplay between the number of distinct measurement operators, the number of measurements per operator, the dimension of the model and the dimension of the signals. Furthermore, we propose a novel and conceptually simple unsupervised learning loss which only requires access to incomplete measurement data and achieves a performance on par with supervised learning when the sufficient condition is verified. We validate our theoretical bounds and demonstrate the advantages of the proposed unsupervised loss compared to previous methods via a series of experiments on various imaging inverse problems, such as accelerated magnetic resonance imaging, compressed sensing and image inpainting.

关键词： Unsupervised learning

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Super-resolution of Fisheye Rectified image based on Deep Multi-Path Cascaded Network 9

Super-resolution of Fisheye Rectified Image based on Deep Mu...

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9th International conference on Big data and Information Analytics, BigDIA 2023

作者： Li, Weili Li, Zhe Wang, Haoming Yin, Xiaoqing Ma, YiQin National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China National University of Defense Technology College of Computer Science Changsha China National University of Defense Technology College of Advanced Interdisciplinary Studies Changsha China

ISBN: (纸本)9798350330076

Fisheye imaging technique plays an important role in various areas. However, fisheye lenses can cause severe image distortion, which requires distortion rectification for these images. However, after distortion rectification, the images will suffer from the problem of resolution reduction, and the degree of resolution reduction varies in different regions. This problem will directly affect the subsequent process of image content analysis. Therefore, it is needed to adopt a super-resolution reconstruction algorithm to enhance the resolution of the fisheye rectified image. This paper designs a multi-path cascaded deep network which performs super-resolution reconstruction of fisheye rectified images. Through the multi-path deep network structure, the features of the fisheye rectified image are learned from multiple aspects at the same time, and the residual learning approach is introduced to further diminish the difficulty of network training and improve the super-resolution results. The mean square error loss is used to supervise the training process of the proposed deep work. The super-resolution reconstruction model is obtained which takes the input low-resolution fisheye rectified image as input and generate the super-resolution result. The results of super-resolution reconstruction experiments demonstrate that the proposed multi-path cascaded deep network can effectively enhance the resolution of fisheye rectified images, and can provide higher quality images for fisheye image analysis. © 2023 IEEE.

关键词： Mean square error

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